Aspiring Data Analyst & ML Practitioner building data science, machine learning, and neural network projects with a strong focus on learning in public, improving project quality, and turning concepts into portfolio‑ready work.
I started with data analytics fundamentals and gradually expanded into machine learning, neural networks, SQL concepts, and project‑based portfolio building.
Right now, I am focused on building stronger repositories, improving presentation quality, and growing from mini‑projects toward more complex real‑world systems.
- Using my Statistics background to understand data, patterns, and model behavior
- Building Machine Learning projects (supervised, unsupervised, model comparison)
- Expanding into Deep Learning / Neural Networks (Perceptron, MLP, ANN on MNIST & Iris)
- Converting datasets into clean notebooks, visualizations, and recruiter‑friendly GitHub repositories
- Preparing for Data Scientist, ML Engineer, and Data Analyst opportunities
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Handwritten digit classification using an Artificial Neural Network (MLP) with TensorFlow/Keras. This project reflects my growth into deep learning, preprocessing, evaluation, and stronger repository presentation. 
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An unsupervised learning mini project using K-Means clustering on a synthetic dataset. It highlights clustering logic, visual interpretation, and ML intuition beyond labeled prediction tasks. 
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Classification project comparing Logistic Regression, Decision Tree, and Random Forest with AUC‑focused evaluation. This repo shows model comparison, evaluation thinking, and recruiter‑friendly storytelling. 
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A SQL mini project demonstrating conversion from unnormalized data to 1NF, 2NF, and 3NF. It adds database‑design thinking to my portfolio beyond notebook‑based ML work. 
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End‑to‑end retail sales analysis on the Superstore dataset using Python, Pandas, Seaborn, Matplotlib, and Excel. Focused on data cleaning, trend analysis, regional comparison, top‑product insights, and visual reporting. 
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| Neural Networks | Perceptron on Iris • MLP on Iris • ANN on MNIST |
| Machine Learning | Bank Loan Approval • K-Means Clustering • Gaussian Naive Bayes • Logistic Regression Projects |
| Data Analysis | Sales Data Analysis • Diamonds EDA • Automated EDA Explorations |
| SQL & Data Design | Normalization Process • Structured data thinking • Database fundamentals |
| More Incoming | More advanced portfolio‑grade projects are under development and will be uploaded soon. |
Currently working on a more advanced Product Recommendation System focused on customer‑product interaction patterns, ranking logic, and stronger end‑to‑end project thinking.
- Moving from mini‑projects toward more complex, portfolio‑grade systems
- Improving GitHub presentation and repository storytelling
- Strengthening ML, DL, analytics, and SQL foundations through practical builds
- Uploading more advanced work soon
- Data analysis with stronger business storytelling
- Machine learning model building and comparison
- Neural networks: Perceptron, MLP, ANN workflows
- Better GitHub repository structure and README design
- SQL concepts and database normalization
- Growing toward capstone‑level, real‑world and Gen AI inspired projects
Learning deeply. Building consistently. Turning data into insight, models, and meaningful progress.